Compare GraphQL and Neo4j Graph Database 
- Users report that Neo4j Graph Database excels in ease of setup with a score of 8.8, making it a preferred choice for those who prioritize quick deployment. In contrast, GraphQL lacks a specific score for ease of setup, indicating potential challenges for users looking for a straightforward implementation.
- Reviewers mention that Neo4j's query language is highly rated at 8.2, which allows for complex queries and efficient data retrieval. On the other hand, GraphQL's query capabilities are less emphasized, leading to concerns about its performance in handling intricate data relationships.
- G2 users highlight Neo4j's strong performance in data management, particularly in data replication and advanced data analytics, which are crucial for businesses that rely on real-time data insights. GraphQL, while flexible, does not provide the same level of built-in analytics features, which may limit its effectiveness in data-heavy environments.
- Users on G2 report that Neo4j's quality of support is rated at 8.5, indicating a robust support system for users. In contrast, GraphQL lacks specific ratings for support, which could lead to uncertainty for potential buyers regarding the assistance available during implementation and troubleshooting.
- Reviewers mention that Neo4j's product direction is rated at 9.0, suggesting a strong commitment to innovation and user feedback. GraphQL, with a lower score of 7.7, may not inspire the same level of confidence in its future development and enhancements.
- Users say that Neo4j's security features, including role-based authorization and authentication, are well-rated at 8.0, providing peace of mind for businesses concerned about data protection. In comparison, GraphQL's security measures are less defined, which could raise concerns for organizations prioritizing data security.
- console.neo4j.io
The helpful things are that it does not complicate stuff and can bring out the exact data we want. It also improves performance speed by aggregating HTTP requests into one which works best with APIs.
Adds a layer of complexity that not a lot of programmers are familiar with. Steep learning curve. Nesting several mutations is a pain when dealing with authorization.
I started using Neo4J to represent various facilities ( of a startup that I work at) across the city as nodes and represent their relationships in terms of mode of commute available and the associated costs. The brilliant thing about Neo4J, or graph DB...
The tooling itself was an issue. We used the neo4j ruby bindings to create an embedded neo4j database in rails. This required us to use jruby instead of regular ruby. Using jruby created a lot of other issues with compatibility with the various gems out...
The helpful things are that it does not complicate stuff and can bring out the exact data we want. It also improves performance speed by aggregating HTTP requests into one which works best with APIs.
I started using Neo4J to represent various facilities ( of a startup that I work at) across the city as nodes and represent their relationships in terms of mode of commute available and the associated costs. The brilliant thing about Neo4J, or graph DB...
Adds a layer of complexity that not a lot of programmers are familiar with. Steep learning curve. Nesting several mutations is a pain when dealing with authorization.
The tooling itself was an issue. We used the neo4j ruby bindings to create an embedded neo4j database in rails. This required us to use jruby instead of regular ruby. Using jruby created a lot of other issues with compatibility with the various gems out...
